A Review of quantum algorithms for prediction of hazardous asteroids

  • Priya Pareshbhai Bhagwakar Department of Computer Engineering, L. D. College of Engineering, Ahmedabad 380015, Gujarat, India
  • Chirag Suryakant Thaker Department of Computer Engineering, L. D. College of Engineering, Ahmedabad 380015, Gujarat, India
  • Hetal A. Joshiara Department of Computer Engineering, L. D. College of Engineering, Ahmedabad 380015, Gujarat, India
Ariticle ID: 1141
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DOI: https://doi.org/10.59400/cai.v2i1.1141
Keywords: quantum computing; quantum machine learning; quantum algorithms; machine learning; deep learning; asteroids; prediction

Abstract

Quantum computing (QC) and quantum machine learning (QML), two emerging technologies, have the potential to completely change how we approach solving difficult problems in physics and astronomy, among other fields. Potentially Hazardous Asteroids (PHAs) can produce a variety of damaging phenomena that put biodiversity and human life at serious risk. Although PHAs have been identified through the use of machine learning (ML) techniques, the current approaches have reached a point of saturation, signaling the need for additional innovation. This paper provides an in-depth examination of various machine learning (ML) and QML techniques for precisely identifying potentially hazardous asteroids. The study attempts to provide information to improve the efficiency and accuracy of asteroid categorization by combining QML techniques like deep learning with a variety of machine learning (ML) algorithms, such as Random Forest and support vector machines. The study highlights weaknesses in existing approaches, including feature selection and model assessment, and suggests directions for further investigation. The results highlight the significance of developing QML techniques to increase the prediction of asteroid hazards, consequently supporting enhanced risk assessment and space exploration efforts. Paper reviews might not be related because the study only looks at generic paper reviews.

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Published
2024-05-17
How to Cite
Bhagwakar, P. P., Thaker, C. S., & Joshiara, H. A. (2024). A Review of quantum algorithms for prediction of hazardous asteroids. Computing and Artificial Intelligence, 2(1), 1141. https://doi.org/10.59400/cai.v2i1.1141
Issue
Vol. 2 No. 1 (2024)
Section
Review

Copyright (c) 2024 Priya Pareshbhai Bhagwakar, Chirag Suryakant Thaker, Hetal A. Joshiara

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